{"product_id":"transfer-in-reinforcement-learning-domains-hardcover","title":"Transfer in Reinforcement Learning Domains - Hardcover","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eMatthew Taylor\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003eReinforcement Learning Background.- Related Work.- Empirical Domains.- Value Function Transfer via Inter-Task Mappings.- Extending Transfer via Inter-Task Mappings.- Transfer between Different Reinforcement Learning Methods.- Learning Inter-Task Mappings.- Conclusion and Future Work.\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eIn reinforcement learning (RL) problems, learning agents sequentially execute actions with the goal of maximizing a reward signal. The RL framework has gained popularity with the development of algorithms capable of mastering increasingly complex problems, but learning difficult tasks is often slow or infeasible when RL agents begin with no prior knowledge. The key insight behind \"transfer learning\" is that generalization may occur not only within tasks, but also across tasks. While transfer has been studied in the psychological literature for many years, the RL community has only recently begun to investigate the benefits of transferring knowledge. This book provides an introduction to the RL transfer problem and discusses methods which demonstrate the promise of this exciting area of research.\u003c\/p\u003e \u003cp\u003eThe key contributions of this book are: \u003c\/p\u003e \u003cul\u003e \u003cul\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eDefinition of the transfer problem in RL domains \u003c\/li\u003e \u003cli\u003eBackground on RL, sufficient to allow a wide audience to understand discussed transfer concepts \u003c\/li\u003e \u003cli\u003eTaxonomy for transfer methods in RL \u003c\/li\u003e \u003cli\u003eSurvey of existing approaches \u003c\/li\u003e \u003cli\u003eIn-depth presentation of selected transfer methods \u003c\/li\u003e \u003cli\u003eDiscussion of key open questions\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003cem\u003eBy way of the research presented in this book, the author has established himself as the pre-eminent worldwide expert on transfer learning in sequential decision making tasks. A particular strength of the research is its very thorough and methodical empirical evaluation, which Matthew presents, motivates, and analyzes clearly in prose throughout the book. Whether this is your initial introduction to the concept of transfer learning, or whether you are a practitioner in the field looking for nuanced details, I trust that you will find this book to be an enjoyable and enlightening read.\u003c\/em\u003e\u003c\/p\u003e \u003cp\u003e\u003cem\u003ePeter Stone, Associate Professor of Computer Science\u003c\/em\u003e\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 230\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.56 x 9.21 x 6.14 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e June 05, 2009\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":47212939084025,"sku":"9783642018817","price":178.18,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0789\/2782\/3097\/files\/eDNiK0sxWnhkZUZvRGRJdllBNUtpQT09.webp?v=1768099080","url":"https:\/\/bookscloud.io\/products\/transfer-in-reinforcement-learning-domains-hardcover","provider":"BooksCloud Book Dropshipping","version":"1.0","type":"link"}